@conference {94, title = {Automatic Domain Identification for Linked Open Data}, booktitle = {2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013}, year = {2013}, pages = {205{\textendash}212}, address = {Atlanta, GA, USA}, abstract = {

Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.

}, keywords = {dataset search, Domain Identification, Linked Open Data Cloud}, doi = {10.1109/WI-IAT.2013.206}, url = {http://dx.doi.org/10.1109/WI-IAT.2013.206}, author = {Sarasi Lalithsena and Pascal Hitzler and Amit Sheth and Prateek Jain} } @article {308, title = {There{\textquoteright}s No Money in Linked Data}, year = {2013}, institution = {DaSe Lab, Department of Computer Science and Engineering, Wright State University}, address = {Dayton, OH, USA}, abstract = {

Linked Data (LD) has been an active research area for more than 6 years and many aspects about publishing, retrieving, linking, and cleaning Linked Data have been investigated. There seems to be a broad and general agreement that in principle LD datasets can be very useful for solving a wide variety of problems ranging from practical industrial analytics to highly specific research problems. Having these notions in mind, we started exploring the use of notable LD datasets such as DBpedia, Freebase, Geonames and others for a commercial application. However, it turns out that using these datasets in realistic settings is not always easy. Surprisingly, in many cases the underlying issues are not technical but legal barriers erected by the LD data publishers. In this paper we argue that these barriers are often not justified, detrimental to both data publishers and users, and are often built without much consideration of their consequences.

}, author = {Prateek Jain and Pascal Hitzler and Krzysztof Janowicz and Chitra Venkatramani} } @inbook {174, title = {Alignment-based querying of linked open data}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2012}, year = {2012}, pages = {807{\textendash}824}, publisher = {Springer}, organization = {Springer}, author = {Joshi, Amit Krishna and Prateek Jain and Pascal Hitzler and Peter Z. Yeh and Kunal Verma and Amit Sheth and Mariana Damova} } @conference {97, title = {Moving beyond SameAs with PLATO: Partonomy detection for Linked Data}, booktitle = {23rd ACM Conference on Hypertext and Social Media, HT {\textquoteright}12}, year = {2012}, pages = {33{\textendash}42}, publisher = {ACM}, organization = {ACM}, address = {Milwaukee, WI, USA}, abstract = {

The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.

Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.

In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.

}, keywords = {Linked Open Data Cloud, Mereology, Part of Relation}, doi = {10.1145/2309996.2310004}, url = {http://doi.acm.org/10.1145/2309996.2310004}, author = {Prateek Jain and Pascal Hitzler and Kunal Verma and Peter Z. Yeh and Amit Sheth}, editor = {Ethan V. Munson and Markus Strohmaier} } @conference {99, title = {Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton}, booktitle = {The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011}, volume = {6643}, year = {2011}, pages = {80{\textendash}92}, publisher = {Springer}, organization = {Springer}, address = {Heraklion, Crete, Greece}, abstract = {

The Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies {\textendash} in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) {\textendash} created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.

}, doi = {10.1007/978-3-642-21034-1_6}, url = {http://dx.doi.org/10.1007/978-3-642-21034-1_6}, author = {Prateek Jain and Peter Z. Yeh and Kunal Verma and Reymonrod G. Vasquez and Mariana Damova and Pascal Hitzler and Amit Sheth}, editor = {Grigoris Antoniou and Marko Grobelnik and Elena Paslaru Bontas Simperl and Bijan Parsia and Dimitris Plexousakis and Pieter De Leenheer and Jeff Z. Pan} } @conference {408, title = {Flexible Bootstrapping-Based Ontology Alignment}, booktitle = {Proceedings of the 5th International Workshop on Ontology Matching (OM-2010), Shanghai, China, November 7, 2010}, volume = {689}, year = {2010}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Shanghai, China}, abstract = {

BLOOMS (Jain et al, ISWC2010, to appear) is an ontology alignment system which, in its core, utilizes the Wikipedia category hierarchy for establishing alignments. In this paper, we present a Plug-and-Play extension to BLOOMS, which allows to flexibly replace or complement the use of Wikipedia by other online or offline resources, including domain-specific ontologies or taxonomies. By making use of automated translation services and of Wikipedia in languages other than English, it makes it possible to apply BLOOMS to alignment tasks where the input ontologies are written in different languages.

}, url = {http://ceur-ws.org/Vol-689/om2010_poster9.pdf}, author = {Prateek Jain and Pascal Hitzler and Amit P. Sheth}, editor = {Pavel Shvaiko and J{\'e}r{\^o}me Euzenat and Fausto Giunchiglia and Heiner Stuckenschmidt and Ming Mao and Isabel F. Cruz} } @conference {114, title = {Linked Data Is Merely More Data}, booktitle = {Linked Data Meets Artificial Intelligence, Papers from the 2010 AAAI Spring Symposium}, year = {2010}, publisher = {AAAI}, organization = {AAAI}, address = {Stanford, California, USA}, abstract = {

In this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked {\textquotedblleft}triple collection,{\textquotedblright} it will only be of very limited bene- fit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.

}, url = {http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/1130}, author = {Prateek Jain and Pascal Hitzler and Peter Z. Yeh and Kunal Verma and Amit Sheth}, editor = {Dan Brickley and Vinay K. Chaudhri and Harry Halpin and Deborah McGuinness} } @conference {111, title = {Ontology Alignment for Linked Open Data}, booktitle = {The Semantic Web - ISWC 2010 - 9th International Semantic Web Conference, ISWC 2010}, volume = {6496}, year = {2010}, pages = {402{\textendash}417}, publisher = {Springer}, organization = {Springer}, address = {Shanghai, China}, abstract = {

The Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.

}, doi = {10.1007/978-3-642-17746-0_26}, url = {http://dx.doi.org/10.1007/978-3-642-17746-0_26}, author = {Prateek Jain and Pascal Hitzler and Amit Sheth and Kunal Verma and Peter Z. Yeh}, editor = {Peter F. Patel-Schneider and Yue Pan and Pascal Hitzler and Peter Mika and Lei Zhang and Jeff Z. Pan and Ian Horrocks and Birte Glimm} }